3.1k citations · 4k across the 20 of their papers we have counts for
21 papers
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
Nicholas Konz, Maciej A. Mazurowski
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain p…
Deep Learning for Breast MRI Style Transfer with Limited Training Data
Shixing Cao, Nicholas Konz, James Duncan +1
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is…
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists
Albert Swiecicki, Nianyi Li, Jonathan O'Donnell +5
A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading syste…
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
Nicholas Konz, Hanxue Gu, Haoyu Dong +1
The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural n…
Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset
Jingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda +7
Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.…
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography
Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima +5
Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with…